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  • Articles  (112)
  • Institute of Electrical and Electronics Engineers (IEEE)  (112)
  • Molecular Diversity Preservation International
  • 2015-2019  (112)
  • 1945-1949
  • 2018  (112)
  • IEEE Geoscience and Remote Sensing Letters  (112)
  • 40722
  • 1
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    [Front cover] (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: Presents the cover/table of contents for this issue of the periodical.
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  • 2
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    Table of contents (2018)
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    Publication Date: 2018-03-28
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  • 3
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    IEEE Geoscience and Remote Sensing Letters publication information (2018)
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    Publication Date: 2018-03-28
    Description: Provides a listing of current staff, committee members and society officers.
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  • 4
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    Seasonal Impact of Adjacency Effects on Ocean Color Radiometry at the AAOT Validation Site (2018)
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    Publication Date: 2018-03-28
    Description: The seasonal impact of adjacency effects (AE) on satellite ocean color data at visible and near-infrared (NIR) wavelengths by the Sea-Viewing Wide Field-of-View Sensor, the Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODISA), the Medium Resolution Imaging Spectrometer, the Ocean and Land Color Instrument, the Operational Land Imager (OLI), and the MultiSpectral Imagery (MSI) was theoretically evaluated at a validation site in the northern Adriatic Sea. The analysis made use of comprehensive simulations accounting for multiple scattering, sea surface roughness, sensor viewing geometry, actual coastline, typical and extreme atmospheric conditions, and the seasonal variability of solar illumination and, land and water optical properties. Results, obtained by relying on the normalization of the radiometric sensitivity of each sensor to the same input radiance, show that the spectral and seasonal impacts of AE considerably vary among sensors. AE significantly exceed the radiometric sensitivity of MSI at its sole blue band in winter, whereas they significantly outdo the noise threshold of OLI and MODISA high-resolution data exclusively in the NIR in summer. Conversely, for all other sensors and for MODISA low-resolution data, AE are particularly significant at NIR bands between March and October and at the blue–green bands in winter.
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  • 5
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    Human Target Localization Algorithm Using Energy Operator and Doppler Processing (2018)
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    Publication Date: 2018-03-28
    Description: In this letter, a localization algorithm, which combines energy operator with Doppler processing, is proposed for Doppler radar human sensing applications. For this algorithm, the energy operator is first used to extract the target components of interest from radar echoes and estimate their instantaneous frequencies (IFs). Then, on the basis of the IF estimation result, Doppler processing is applied to synthesize the target movement trajectories. Compared with the traditional localization methods, the proposed algorithm can more precisely estimate the target movement trajectory. Besides, it can further avoid the frequency ambiguity issue, and thus can be very promising for multitarget sensing applications. Experimental results are shown to demonstrate the performance of the proposed algorithm.
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  • 6
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    Asymmetric Adaptation of Deep Features for Cross-Domain Classification in Remote Sensing Imagery (2018)
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    Publication Date: 2018-03-28
    Description: In this letter, we introduce an asymmetric adaptation neural network (AANN) method for cross-domain classification in remote sensing images. Before the adaptation process, we feed the features obtained from a pretrained convolutional neural network to a denoising autoencoder (DAE) to perform dimensionality reduction. Then the first hidden layer of AANN (placed on the top of DAE) maps the labeled source data to the target space, while the subsequent layers control the separation between the available land-cover classes. To learn its weights, the network minimizes an objective function composed of two losses related to the distance between the source and target data distributions and class separation. The results of experiments conducted on six scenarios built from three benchmark scene remote sensing data sets (i.e., Merced, KSA, and AID data sets) are reported and discussed.
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  • 7
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    Brewster Angle Damping Observed in the TerraSAR-X Synthetic Aperture Radar Images of Man-Made Targets (2018)
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    Publication Date: 2018-03-28
    Description: This letter shows the phenomena of Brewster angle damping and its implication observed in the synthetic aperture radar (SAR) images of concrete constructions, such as a bridge and seawalls over the sea. The Fresnel reflection coefficient of concrete material is close to zero at the Brewster angle for X-band V-polarization microwave. The TerraSAR-X images of Tokyo Bay, Japan, at small incidence angles (20.1°–21.4°) showed strong double-bounce reflection between the sea surface and coastal structure with HH-polarization, whereas very little radar backscatter was observed with VV-polarization. The same little radar backscatter was seen in the images of concrete walls on ground and swamp areas covered with reeds. This effect is illustrated with HH/VV intensity and phase difference images, and ground survey data; its implication is also suggested for a better understanding of polarimetric SAR images.
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  • 8
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    Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: Accurate and fast infrared (IR) foreground object detection is one of the most significant issues to be solved due to its important meaning for IR target recognition, IR precise guidance, IR video surveillance, and so on. A common approach for such tasks is “background subtraction,” which aims to detect foreground object through background modeling. Thus far, many background subtraction methods have been proposed and have achieved good performance. However, due to the special characteristics of IR images, a few algorithms are suitable for IR foreground object detection. Recently, features learned from convolutional neural networks (CNNs) have demonstrated great success in many vision tasks, such as classification and recognition. In this letter, we propose a novel multiscale fully convolutional network architecture for IR foreground object detection. Given a CNN model pretrained on a large-scale image data set, our method takes output features from different layers of the network. With features from multiple scales, our feature representation contains both category-level semantics and fine-grain details. The experimental results on IR image sequences show that the proposed method achieves the state-of-the-art performance while operating in real time.
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  • 9
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    Unusual Premonsoon Eddy and Kelvin Wave Activities in the Bay of Bengal During Indian Summer Monsoon Deficit in June 2009 and 2012 (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: An investigation of the eddy and coastal Kelvin wave activities in the Bay of Bengal (BoB) is carried out during premonsoon season in two years of Indian summer monsoon deficit in June (2009 and 2012), occurred in the recent warming hiatus period. Using altimeter observations, our study reveals that over the northern BoB cyclonic eddy kinetic energy is reduced by 35% and 50% from the climatology during premonsoon seasons in 2009 and 2012, respectively, while the cyclonic eddy area is reduced by 18% and 24%, respectively. A concurrent reduction is observed in the first upwelling Kelvin wave (uKW) activities in the eastern equatorial Indian Ocean as well as in the coastal BoB for these years. The reduction in the generation of the first uKW in the eastern equatorial Indian Ocean is attributed to the westerly wind anomalies in January–March of these years. Additionally, meridional wind stress anomalies during March–April in these years are found to be southerly, causing anomalous coastal downwelling in the eastern rim of BoB. This coastal downwelling blocks the propagation of the first uKW. The decrease in the first uKW activities in the coastal waveguide of the BoB reduces the radiation of upwelling Rossby waves, thereby decreasing the cyclonic eddy activities in the northern BoB. The results from this letter could be helpful for further understanding of upper ocean mixing processes in the BoB during monsoon deficit years.
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  • 10
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    A Bi-Iterative Model for Electromagnetic Scattering From a PEC Object Partially Buried in Rough Sea Surface (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: A bi-iterative physical optics (PO) method is presented in this letter to investigate the composite scattering from a perfectly electric conducting object partially buried in the dielectric rough sea surface. In the present method, both the scatterings of a partially buried object and the underlying sea surface are calculated by the PO method. And a bi-iterative strategy is considered, including the mutual interaction among points on the object in direct scattering and the mutual interaction between the object and the sea surface in coupling scattering. In addition, the coupling interaction between the partially buried object and the sea surface contains two parts: 1) the upper surface of the sea surface and the upper part of the object and 2) the lower surface of sea surface and the lower part of the object. In numerical simulations, the bistatic normalized radar cross sections of the composite model are computed by the bi-iterative PO method and are compared with those by the conventional method of moments for different object types and polarizations. The results show that the proposed method has a good accuracy and can greatly reduce the computational time and memory requirement.
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  • 11
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    Tensorization of Multifrequency PolSAR Data for Classification Using an Autoencoder Network (2018)
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    Publication Date: 2018-03-28
    Description: A novel tensorization framework is proposed, which utilizes the Kronecker product to combine multifrequency polarimetric synthetic aperture radar data in conjunction with an artificial neural network (ANN) for classification. The ANN comprises of two stages, where an unsupervised stochastic sampling autoencoder learns an efficient representation and a supervised feed forward network performs classification. The proposed framework is demonstrated using multifrequency (C-, L-, and P-bands) data sets collected by the AIRSAR system. The classification performance of single tensor product of dual- and triple-band combinations is evaluated. It is observed that the classification accuracy of the tensor products outperforms single, as well as, the simple augmentation of the frequency bands.
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  • 12
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    Focus High-Resolution Highly Squint SAR Data Using Azimuth-Variant Residual RCMC and Extended Nonlinear Chirp Scaling Based on a New Circle Model (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: The combination of linear range walk correction and keystone transform is a good choice to focus high-resolution highly squint synthetic aperture radar (SAR) data because it is an effective way to remove linear range cell migration (RCM) completely and mitigate range–azimuth coupling. However, the results of this kind of imaging algorithm produce 2-D-variant residual RCM and variant-dependence Doppler phases. To obtain high-quality SAR image, an improved imaging algorithm using an azimuth-variant residual RCM correction (RCMC) and an extended nonlinear chirp scaling (ENLCS) is proposed in this letter. A new circle model is constructed to analyze the azimuth-variant properties of the residual high-order RCM and the Doppler phases. Based on this circle model, an azimuth-variant residual RCMC is implemented by multiplying a fourth-order phase function, and an improved ENLCS is derived to accomplish the azimuth equalization for azimuth compression. Simulation results validate the excellent performance of the proposed algorithm.
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  • 13
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    Adaptive Probability Thresholding in Automated Ice and Open Water Detection From RADARSAT-2 Images (2018)
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    Publication Date: 2018-03-28
    Description: In this letter, we introduce adaptive probability thresholding in addition to our previously developed technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dual-polarization HH–HV images. Situations where the probability threshold needs to be modified were identified based on the analysis of misclassified ice and water samples when the static probability threshold of 0.95 is applied. We found that with the use of the proposed approach, the fraction of misclassified ice samples decreased from 0.98% to 0.24% and the fraction of misclassified water samples decreased from 0.35% to 0.09% in the most clean verification scenario against Canadian Ice Service Image Analysis pure ice and water data, while the fraction of correctly classified ice and water samples did not decrease appreciably, from 72.2% to 65.4%. The developed approach will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System.
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  • 14
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    SAR Focus Theory of Complicated Range Migration Signatures Due to Moving Targets (2018)
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    Publication Date: 2018-03-28
    Description: Recent studies have revealed that the residual range migration effects of synthetic aperture radar (SAR) imagery smears induced by moving targets can exhibit complicated shapes that are not limited to that of parabolas. This letter demonstrates that automatic focusing methods can remove such range migration effects by estimating and compensating for phase errors directly in the radar video phase history domain. This approach is validated using measured Ku-band SAR clutter data containing buildings and foliage.
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  • 15
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    Target Detection by Exploiting Superpixel-Level Statistical Dissimilarity for SAR Imagery (2018)
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    Publication Date: 2018-03-28
    Description: In this letter, we propose a superpixel-level target detection approach for synthetic aperture radar (SAR) images. With superpixel segmentation, SAR image is divided into meaningful patches and more statistical information can be provided in superpixels compared with single pixels. The statistical difference between target and clutter superpixels can be measured with the intensity distributions of pixels in them. With the assumption of SAR data obeying Gamma distribution, the superpixel dissimilarity is defined. With this basis, the global and local contrast can be obtained and integrated to enhance target and suppress clutter simultaneously. Thus, better target detection performance can be achieved. Different from traditional target detection schemes based on backscattering difference between target and clutter pixels, the proposed method relies on the statistical difference of superpixels. The effectiveness of the proposed method can be demonstrated with experimental results on real SAR images.
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  • 16
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    Temporal Analysis of S-Band Microwave Backscattering From an Inland Reservoir Surface (2018)
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    Publication Date: 2018-03-28
    Description: This letter presents an analysis of the temporal characteristics of electromagnetic waves scattered from a time-varying reservoir surface at low grazing angles. The data collection campaigns were conducted using a polarimetric S-band radar at Wachusett Reservoir in MA, USA, and VV and HH polarized radar returns were simultaneously captured. The temporal behavior of the backscattering from the reservoir surface was analyzed for 180 distinct radar geometries, focusing in particular on the impact of polarization, radar geometry, and wind condition. To understand the shape of the Doppler spectrum, the power spectral density is estimated by a periodogram. In addition, decorrelation time, Doppler centroid, and variance are estimated and compared with the associated Doppler spectral width and peak Doppler frequency. Results show that Doppler spectral width, decorrelation time, and the standard deviation of Doppler spectra are correlated. In addition, the Doppler frequency shift induced by the motion of the water surface is analyzed by peak Doppler frequency and Doppler centroid, which show dependence on radar geometry and wind direction.
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  • 17
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    Anomaly Detection of Hyperspectral Imagery Using Modified Collaborative Representation (2018)
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    Publication Date: 2018-03-28
    Description: Hyperspectral images in remote sensing systems with rich spatial and spectral information provide an opportunity for researchers to discover the world. Anomaly detection is one of the most interesting topics over the last two decades in hyperspectral imagery (HSI). In this letter, we propose a modified collaborative-representation-based with outlier removal anomaly detector (CRBORAD) for anomaly detection. We use both spectral and spatial information for detecting anomalies since that is more precise than using only spectral information. The proposed detector can adaptively estimate the background by its adjacent pixels within a sliding dual-window. We remove outlier pixels that are significantly different from majority of pixels, before estimating background pixels. It can lead us to precise detection of anomalies in subsequent stages. By subtracting the predicted background from the original HSI, the residual image is resulted and anomalies can be determined, finally. Kernel extension of the proposed approach is also presented. CRBORAD results on San Diego airport and the Rochester Institute of Technology data are illustrated using intuitive images, receiver operating characteristic curves, and area under curve values. The results are compared with four popular and previous methods and prove the superiority of the proposed CRBORAD method.
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  • 18
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    Hyperspectral Image Classification With Discriminative Kernel Collaborative Representation and Tikhonov Regularization (2018)
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    Publication Date: 2018-03-28
    Description: Recently, collaborative representation has received much attention in the hyperspectral image (HSI) classification due to its simplicity and effectiveness. However, the existing collaborative representation-based HSI classification methods ignore the correlation among different classes. To overcome this problem, we propose a discriminative kernel collaborative representation and Tikhonov regularization method (DKCRT) for HSI classification, which can make the kernel collaborative representation of different classes to be more discriminative. Specifically, the kernel trick is adopted to map the original HSI into a high space to improve the class separability. Besides, distance-weighted kernel Tikhonov regularization is adopted to enforce these training samples to have large representation coefficients, which are similar to the test sample in the high-dimensional feature space. Moreover, we add a discriminative regularization term to further enhance the separability of different classes, which can take the correlation among different classes into consideration. Furthermore, to take the spatial information of HSI into consideration, we extend the DKCRT to a joint version named JDKCRT. Experiments on real HSIs demonstrate the efficiency of the proposed DKCRT and JDKCRT.
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  • 19
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    Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation (2018)
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    Publication Date: 2018-03-28
    Description: Retrieved soil moisture estimates from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) radiometer are assimilated into the Noah land surface model (LSM) within the NASA Land Information System. The experimental testbed is based on a real-time LSM system produced by the NASA Short-Term Prediction Research and Transition Center. A nonlocalized cumulative distribution function-matching bias correction (BC) is applied to the SMAP retrievals, with separate correction curves calculated based on soil texture categories. We show that the assimilation of SMAP soil moisture retrievals with nonlocalized BC can mitigate two types of artifacts due to spatially varying errors in the forcing data from: 1) bad point (rain gauge) data and 2) strong gradients along the eastern U.S.–Canada border, resulting from blending different observing systems.
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  • 20
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    Quantitative Stability Analysis of Ground Penetrating Radar Systems (2018)
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    Publication Date: 2018-03-28
    Description: The hardware instability of a ground penetrating radar (GPR) system has a severe impact on the quantitative analysis of GPR data, which is aimed for material characterization and subsurface monitoring. In this letter, an instability index is proposed to quantify the stability performance of a GPR system and the influences of the GPR system type, warm-up time, environmental noise, and the antenna vibration on it are evaluated through a series of laboratory experiments on a sandbox model. It is found that the GPR signal recorded by a stepped-frequency GPR system based on a vector network analyzer is much more stable than that by a commercial impulse GPR system at a cost of more sweep time. A warm-up time of several minutes is enough for an impulse GPR system. Environmental noise has a negligible influence on the stability performance of a GPR system. Mechanical vibrations of GPR antennas have a severe impact on the stability performance of the GPR system, and the instability index and timing jitter can be increased by more than one order of magnitude in a vibrating condition over those in a static condition. The instability index of the direct signal has a negligible difference with that of the reflection signal from a metal plate; thus, a simple measurement of direct signal on the ground surface is suggested for the evaluation of the instability of a GPR system in field in the future.
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  • 21
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    Knowledge-Aided Covariance Matrix Estimation via Kronecker Product Expansions for Airborne STAP (2018)
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    Publication Date: 2018-03-28
    Description: This letter proposes a new approach for knowledge-aided estimation of structured clutter covariance matrices (CCMs) in airborne radar systems with limited training data. First, we model the CCM in space–time adaptive processing (STAP) as a sum of low-rank Kronecker products. We then apply a permutation operation to convert the Kronecker factors into linear structures and propose a novel CCM estimation method under the maximum-likelihood framework. Employing a proximal gradient algorithm, the proposed method simultaneously exploits the knowledge about the clutter and the Kronecker structure of the CCM. We finally evaluate the performance of the proposed method using real data from airborne STAP.
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  • 22
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    Automatic Water-Body Segmentation From High-Resolution Satellite Images via Deep Networks (2018)
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    Publication Date: 2018-03-28
    Description: Water-body segmentation is an important issue in remote sensing and image interpretation. Classic methods for counteracting this problem usually include the construction of index features by combining different spectra, however, these methods are essentially rule-based and fail to take advantage of context information. Additionally, as the quality of image resolution improves, these methods are proved to be inadequate. With the rise of convolutional neural networks (CNN), the level of research about segmentation has taken a huge leap, but the field is still facing an increasing demand for data and the problem of blurring boundaries. In this letter, a new segmentation network called restricted receptive field deconvolution network (RRF DeconvNet) is proposed, with which to extract water bodies from high-resolution remote sensing images. Compared with natural images, remote sensing images have a weaker pixel neighborhood relativity; in consideration of this challenge, an RRF DeconvNet compresses the redundant layers in the original DeconvNet and no longer relies on a pretrained model. In addition, to tackle the blurring boundaries that occur in CNN, a new loss function called edges weighting loss is proposed to train segmentation networks, which has been shown to significantly sharpen the segmentation boundaries in results. Experiments, based on Google Earth images for water-body segmentation, are presented in this letter to prove our method.
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  • 23
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    Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure (2018)
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    Publication Date: 2018-03-28
    Description: Infrared (IR) small target detection with high detection rate, low false alarm rate, and high detection speed has a significant value, but it is usually very difficult since the small targets are usually very dim and may be easily drowned in different types of interferences. Current algorithms cannot effectively enhance real targets and suppress all the types of interferences simultaneously. In this letter, a multiscale detection algorithm utilizing the relative local contrast measure (RLCM) is proposed. It has a simple structure: first, the multiscale RLCM is calculated for each pixel of the raw IR image to enhance real targets and suppress all the types of interferences simultaneously; then, an adaptive threshold is applied to extract real targets. Experimental results show that the proposed algorithm can deal with different sizes of small targets under complex backgrounds and has a better effectiveness and robustness against existing algorithms. Besides, the proposed algorithm has the potential of parallel processing, which is very useful for improving the detection speed.
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  • 24
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    IEEE Geoscience and Remote Sensing Letters Institutional Listings (2018)
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    Publication Date: 2018-03-28
    Description: Advertisements.
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    IEEE Geoscience and Remote Sensing Letters information for authors (2018)
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    Publication Date: 2018-03-28
    Description: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
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    An Image Transform Based on Temporal Decomposition (2018)
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    Publication Date: 2018-03-28
    Description: Today, very dense synthetic aperture radar (SAR) time series are available through the framework of the European Copernicus Programme. These time series require innovative processing and preprocessing approaches including novel speckle suppression algorithms. Here we propose an image transform for hypertemporal SAR image time stacks. This proposed image transform relies on the temporal patterns only, and therefore fully preserves the spatial resolution. Specifically, we explore the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the high-frequency components are corresponding to speckle, these effects can be isolated and removed. We assessed the speckle filtering performance of the transform using hypertemporal Sentinel-1 data acquired over central Germany comprising 53 scenes. We investigated speckle suppression, ratio images, and edge preservation. For the latter, a novel approach was developed. Our findings suggest that EMD features speckle suppression capabilities similar to that of the Quegan filter while preserving the original image resolution.
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    Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification (2018)
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    Publication Date: 2018-03-28
    Description: Cross-scene hyperspectral image (HSI) classification has recently become increasingly popular due to its crucial use in various applications. It poses great challenges to existing domain adaptation methods because of the data set shift, that is, two scenes exhibit huge distribution discrepancy. To tackle this problem, we propose a new domain adaptation method called hyperspectral feature adaptation and augmentation (HFAA) for cross-scene HSI classification. The proposed HFAA method learns a common subspace by introducing two different projection matrices to extract the transferable knowledge from the source domain to the target domain. To further enhance the common subspace representation, we propose to augment it by the feature selection strategy. HFAA can make full use of the original features from both source and target domains, and increase the similarity of the samples with the same label from the two domains. Our proposed HFAA method achieves compact but discriminative feature representations, which make it well suited for data sets with a large number of classes and huge interclass ambiguity. Experimental results on the Earth Observing 1 hyperspectral data set show that HFAA can produce state-of-the-art performance and surpass previous methods.
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    Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting (2018)
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    Publication Date: 2018-03-28
    Description: In this letter, the recently developed extreme gradient boosting (Xgboost) classifier is implemented in a very high resolution (VHR) object-based urban land use–land cover application. In detail, we investigated the sensitivity of Xgboost to various sample sizes, as well as to feature selection (FS) by applying a standard technique, correlation-based FS. We compared Xgboost with benchmark classifiers such as random forest (RF) and support vector machines (SVMs). The methods are applied to VHR imagery of two sub-Saharan cities of Dakar and Ouagadougou and the village of Vaihingen, Germany. The results demonstrate that Xgboost parameterized with a Bayesian procedure, systematically outperformed RF and SVM, mainly in larger sample sizes.
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    PolSAR Image Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network (2018)
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    Publication Date: 2018-03-28
    Description: Polarimetric synthetic aperture radar (PolSAR) image classification is an important application. Advanced deep learning techniques represented by deep convolutional neural network (CNN) have been utilized to enhance the classification performance. One current challenge is how to adapt deep CNN classifier for PolSAR classification with limited training samples, while keeping good generalization performance. This letter attempts to contribute to this problem. The core idea is to incorporate expert knowledge of target scattering mechanism interpretation and polarimetric feature mining to assist deep CNN classifier training and improve the final classification performance. A polarimetric-feature-driven deep CNN classification scheme is established. Both classical roll-invariant polarimetric features and hidden polarimetric features in the rotation domain are used to drive the proposed deep CNN model. Comparison studies validate the efficiency and superiority of the proposal. For the benchmark AIRSAR data, the proposed method achieves the state-of-the-art classification accuracy. Meanwhile, the convergence speed from the proposed polarimetric-feature-driven CNN approach is about 2.3 times faster than the normal CNN method. For multitemporal UAVSAR data sets, the proposed scheme achieves comparably high classification accuracy as the normal CNN method for train-used temporal data, while for train-not-used data it obtains an average of 4.86% higher overall accuracy than the normal CNN method. Furthermore, the proposed strategy can also produce very promising classification accuracy even with very limited training samples.
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    Unsupervised Hyperspectral Remote Sensing Image Clustering Based on Adaptive Density (2018)
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    Publication Date: 2018-03-28
    Description: Hyperspectral remote sensing image (HSI) clustering can be defined as the process of segmenting pixels into different sets that satisfy the requirement that the differences between sets are much greater than the differences within sets. According to the fast density peak-based clustering algorithm, we propose an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels. For the metric of the density, we present an adaptive-bandwidth probability density function using pixel numbers as the input and the calculated pixel local density as the output, which determines the bandwidth on the basis of the Gaussian assumption. For the metric of the distance, in order to obtain a pixel-level spectral distance, we calculate the Euclidean distance between pixel vectors from the multiple bands. In the proposed approach: 1) use the least-squares method for the curve fitting of the two results; 2) eliminate outliers based on the Pauta criterion; 3) adopt regression calculation; and 4) obtain the cluster centers according to the classification criteria of the local density and the distance between pixel vectors. The other noncluster center points are clustered based on their similarities with the cluster centers by iteration. Finally, we compare the results with those of other unsupervised clustering methods and the reference data sets.
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    Doppler Profile Tracing Using MPCF on MU Radar and Sodar: Performance Analysis (2018)
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    Publication Date: 2018-03-28
    Description: Wind profilers (WPs) are ground-based pulsed Doppler radars operating in the UHF/very high frequency (VHF) bands. They use backscatter from clear-air turbulence as a tracer of background wind in the troposphere and up to mesosphere. Wind profiling systems range from large research radar systems to smaller operational units. In recent times, multiparameter cost function (MPCF) has emerged as a computationally efficient Doppler profile estimation method. It has been claimed that the MPCF method can be easily migrated to any vertically sounding clear-air wind profiling system that works in Doppler beam swinging mode. In order to investigate this claim, MPCF was applied to the following wind profiling systems: the middle and upper atmosphere (MU) radar, located at Shigaraki, Japan, which is an active phased array VHF radar system with 475 transceivers, and Doppler sodar, located at Pune, India, which uses acoustic frequency to obtain echoes in the planetary boundary layer. Both these systems are complementary in the sense that they cover ranges from ground to about 80 km. During the experimentation, the MPCF algorithm did not need any change of parameters except the matching of the data reading formats. The results of MPCF on the MU radar were validated with Radiosonde data. These results indicate that the MPCF works seamlessly on all types of WP systems irrespective of the carrier, range, and radar type.
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    Frequency Domain Inverse Profiling of Buried Dielectric Elliptical-Cylindrical Objects Using Evolutionary Programming (2018)
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    Publication Date: 2018-03-28
    Description: An efficient method based on the evolutionary programming (EP) technique is proposed for inverse profiling of 2-D buried dielectric objects with elliptical cross sections. In particular, EP with Cauchy mutation operator (EP-CMO), as its first reported implementation to inverse problems, is utilized as a stochastic optimization tool for quantitatively reconstructing buried objects. Moreover, the method of moments technique in conjunction with conjugate gradient-fast Fourier transform method is used, as a fast and simple frequency domain forward solver, in each iteration of the proposed method. Numerical results for different case studies are presented and analyzed. To assess the proposed EP-CMO method, the results are also compared statistically with that of three other well-known optimization techniques, namely, EP with Gaussian mutation, particle swarm optimization, and genetic algorithms. The results reveal that EP-CMO is a significantly more robust and efficient optimization tool in reconstruction of this class of buried objects.
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    Detection of Magnetic Anomaly Signal Based on Information Entropy of Differential Signal (2018)
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    Publication Date: 2018-03-28
    Description: Magnetic anomaly detection is an effective approach for detecting the visually obscured ferromagnetic target, and its performance is mainly limited by background geomagnetic noise. In contrast to the traditional detection methods that rely on several a priori assumptions regarding the target or the probability of magnetic noise consisting of external geomagnetic noise and intrinsic sensor noise, we present, in this letter, a new estimator of information entropy for differential signal acquired by a pair of magnetic sensors to detect any changes in the magnetic noise pattern. First, the magnetic noise probability density function (PDF) of differential signal is estimated by using the kernel smoothing method. Then, the minimum entropy detector based on the magnetic noise PDF of differential signal is used to detect the magnetic anomaly target. Finally, according to the probabilities of false alarm, the detection threshold can be obtained to be used for abnormal judgment. In order to verify the effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that the proposed method has better detection performance than that of traditional methods.
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    A Saliency-Based Band Selection Approach for Hyperspectral Imagery Inspired by Scale Selection (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: This letter presents a band selection method relying on saliency bands and scale selection (SBSS). The SBSS method is used to excavate the hidden information of hyperspectral images effectively, while its underlying assumptions are: 1) it is reasonable to combine spectral and spatial information to excavate the intrinsic property of a hyperspectral image; 2) there are some saliency bands that can represent a hyperspectral image without significant information loss in data exploitation; and 3) saliency, scale, and image description have an intrinsic connection. The computational complexity of the SBSS method is linear, and experimental results demonstrate that the proposed method obtains competitively good results compared with other state-of-the-art band selection techniques, in terms of classification accuracy.
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    A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-03-28
    Description: The lack of proper class discrimination among the hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this letter proposes an optimal geometry-aware transformation for enhancing the classification accuracy. The underlying idea of this method is to obtain a linear projection matrix by solving a nonlinear objective function based on the intrinsic geometrical structure of the data. The objective function is constructed to quantify the discrimination between the points from dissimilar classes on the projected data space. Then, the obtained projection matrix is used to linearly map the data to more discriminative space. The effectiveness of the proposed transformation is illustrated with three benchmark real-world HS data sets. The experiments reveal that the classification and dimensionality reduction methods on the projected discriminative space outperform their counterpart in the original space.
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    Nonrigid Image Registration for Low-Altitude SUAV Images With Large Viewpoint Changes (2018)
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    Publication Date: 2018-03-28
    Description: Low-altitude aerial photography using small unmanned aerial vehicles (SUAVs) with large viewpoint changes causes nonrigid distortions and low overlap ratios. We present a nonrigid feature-based low-altitude SUAV image-registration method. The key idea of our method is to maintain a high matching ratio on inliers while taking advantage of outliers for varying the warping grids. Thus, accurate image transformation over the overlapping areas as well as a good approximation of the real transformation over the nonoverlapping areas can be obtained. Experiments on feature matching and image registration are performed using 42 pairs of SUAV images. Our method exhibited a favorable performance as compared with four state-of-the-art methods, even with up to 80% outliers.
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    Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM (2018)
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    Publication Date: 2018-02-28
    Description: Recently, approaches based on fully convolutional networks (FCN) have achieved state-of-the-art performance in the semantic segmentation of very high resolution (VHR) remotely sensed images. One central issue in this method is the loss of detailed information due to downsampling operations in FCN. To solve this problem, we introduce the maximum fusion strategy that effectively combines semantic information from deep layers and detailed information from shallow layers. Furthermore, this letter develops a powerful backend to enhance the result of FCN by leveraging the digital surface model, which provides height information for VHR images. The proposed semantic segmentation scheme has achieved an overall accuracy of 90.6% on the ISPRS Vaihingen benchmark.
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    Front Cover (2018)
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    IEEE Geoscience and Remote Sensing Letters publication information (2018)
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    Table of contents (2018)
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    List of Reviewers (2018)
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    Hybrid Stripmap–ScanSAR Interferometry: Extension to the X-Band COSMO-SkyMed Data (2018)
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    Publication Date: 2018-02-28
    Description: This letter aims to investigate the applicability of the hybrid stripmap–ScanSAR differential synthetic aperture radar interferometry to data collected by the X-band RADAR sensors of the Italian COSMO-SkyMed (CSK) constellation. The presented analysis relies on the experiments carried out on two sets of stripmap and ScanSAR CSK images collected over the Chuquicamata copper mine in Chile and the Mojave Desert in Southern California, respectively. As a result of our investigation, a spurious phase modulation has been recognized in the generated hybrid stripmap–ScanSAR interferograms along the azimuth direction. This effect is due to the specific staggered topology of the used active phased array antennas that introduces half-wavelength residuals during the electronic antenna steering in the elevation plane. A straightforward solution to compensate for such spurious phase modulation effects has been provided, and the achieved results demonstrate its effectiveness. The outcomes of our analysis may be relevant in the perspective of the future CSK Second Generation Program.
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    Improving the Efficiency of Hydrological Investigations by Submersible Ground Penetrating Radar (2018)
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    Publication Date: 2018-02-28
    Description: This letter presents a newly developed method of submersible ground penetrating radar, which is based on the experimentally determined shift of the frequency spectra of probing signals generated by antennas submerged in water. Experimental results for shift limits of probing spectra signals of frequencies generated by antennas placed in an aqueous environment are presented here. Shift limits of frequency spectrum of electromagnetic radar during probing from aqueous area of varying conductivity are determined, in particular for the most typical electrical conductivity of rivers in Russia and adjoining countries, including rivers and lakes in Yakutia. The methodology of submersible ground penetrating radar (GPR) survey of water features is presented. The possibility of increasing the depth of sediment by experimental antennas which are using water surface as a natural dielectric reflector is shown. Established criteria recommended for use in interpreting submersible GPR results and bottom sediments of fresh waters reservoirs, amplitude–frequency spectral criteria, phase–frequency spectral criteria, and the criteria of isobathic amplitudes ratio are explained. The parameters of the erosion of bottom sediments near the pile trestle due to channel processes of Lena River are determined.
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    A Massively Parallel Deep Rule-Based Ensemble Classifier for Remote Sensing Scenes (2018)
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    Publication Date: 2018-02-28
    Description: In this letter, we propose a new approach for remote sensing scene classification by creating an ensemble of the recently introduced massively parallel deep (fuzzy) rule-based (DRB) classifiers trained with different levels of spatial information separately. Each DRB classifier consists of a massively parallel set of human-interpretable, transparent zero-order fuzzy IF…THEN… rules with a prototype-based nature. The DRB classifier can self-organize “from scratch” and self-evolve its structure. By employing the pretrained deep convolution neural network as the feature descriptor, the proposed DRB ensemble is able to exhibit human-level performance through a transparent and parallelizable training process. Numerical examples using benchmark data set demonstrate the superior accuracy of the proposed approach together with human-interpretable fuzzy rules autonomously generated by the DRB classifier.
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    Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation (2018)
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    Publication Date: 2018-02-28
    Description: The joint sparse representation (JSR)-based classifier assumes that pixels in a local window can be jointly and sparsely represented by a dictionary constructed by the training samples. The class label of each pixel can be decided according to the representation residual. However, once the local window of each pixel includes pixels from different classes, the performance of the JSR classifier may be seriously decreased. Since correlation coefficient (CC) is able to measure the spectral similarity among different pixels efficiently, this letter proposes a new classification method via fusing CC and JSR, which attempts to use the within-class similarity between training and test samples while decreasing the between-class interference. First, the CCs among the training and test samples are calculated. Then, the JSR-based classifier is used to obtain the representation residuals of different pixels. Finally, a regularization parameter $lambda $ is introduced to achieve the balance between the JSR and the CC. Experimental results obtained on the Indian Pines data set demonstrate the competitive performance of the proposed approach with respect to other widely used classifiers.
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    Design of a Configurable Monitoring Station for Scintillations by Means of a GNSS Software Radio Receiver (2018)
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    Publication Date: 2018-02-28
    Description: This letter addresses the design and implementation of a monitoring station for Global Navigation Satellite Systems signals based on the software-defined radio paradigm. The monitoring platform exploits a digital data grabber based on the use of Universal Software Radio Peripheral devices and a satellite navigation fully software receiver; with respect to a traditional commercial receiver, this implementation solution grants a higher level of flexibility for the processing strategy, enabling the possibility of a deeper analysis of the signals in case of meaningful events, such as ionospheric scintillations or radio frequency interference, through the storage of raw samples. Such an implementation approach yields valuable advantages in critical and remote areas, such as polar regions, where resources are limited and installation, maintenance, and replacement of hardware may be critical.
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    An Automatic Evaluation Platform for Feature Matching Algorithms Based on an Orbital Optical Pushbroom Stereo Imaging System (2018)
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    Publication Date: 2018-02-28
    Description: Thanks to the high robustness of feature-based matching algorithms, they can be applied in remote sensing (RS) applications with complex image changes. However, evaluating feature matching algorithms on RS images is still challenging, because creating ground truth matching data sets of RS images costs a lot of both computing time and human operator time. Therefore, in this letter, we present an evaluation platform for simulating RS ground truth data sets of feature-points correspondences based on a customizable orbital optical pushbroom stereo imaging system. With the help of the proposed platform, evaluating feature matching algorithms could be fully automatic and customized. The performance of three state-of-the-art feature matching algorithms based on local transformation constraint is evaluated and discussed on the proposed platform with comprehensive experiments. The evaluation results of the platform are also compared with the manual evaluation results of 10 pairs of real RS stereo images. The evaluation results show that the proposed platform indeed offers an efficient way for evaluating RS feature matching algorithms.
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    Application of a Variational Mode Decomposition-Based Instantaneous Centroid Estimation Method to a Carbonate Reservoir in China (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: A novel hydrocarbon detection technique named the variational mode decomposition (VMD)-based instantaneous centroid method is proposed in this letter. It reveals frequency-dependent amplitude anomalies that may reflect some details deeply buried within the intrinsic mode functions (IMFs) in particular frequency bands. Instantaneous amplitude and instantaneous frequency information from each IMF are used to generate each IMF instantaneous centroid. A weighted correlation scheme is employed to generate the VMD-based instantaneous centroid volume for a seismic trace. Model testing and field data from a carbonate reservoir in China illustrate that the VMD-based instantaneous centroid method can provide a better hydrocarbon-prone interpretation with a higher resolution and accuracy. Comparisons between the VMD-based instantaneous centroid method and the short-time Fourier transform, and continuous wavelet transform and prestack wave impedance inversion technology indicate that the proposed method is more convenient and can effectively target gas reservoirs. This letter presents a complementary approach to current methods used to extract frequency-dependent amplitude anomaly information.
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    Campo Verde Database: Seeking to Improve Agricultural Remote Sensing of Tropical Areas (2018)
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    Publication Date: 2018-02-28
    Description: In tropical/subtropical regions, the favorable climate associated with the use of agricultural technologies, such as no tillage, minimum cultivation, irrigation, early varieties, desiccants, flowering inducing, and crop rotation, makes agriculture highly dynamic. In this letter, we present the Campo Verde agricultural database. The purpose of creating and sharing these data is to foster advancement of remote sensing technology in areas of tropical agriculture, primarily the development and testing of methods for crop recognition and agricultural mapping. Campo Verde is a municipality of Mato Grosso state, localized in the Cerrado (Brazilian Savanna) biome, in central west Brazil. Soybean, maize, and cotton are the primary crops cultivated in this region. Double cropping systems are widely adopted in this area. There is also livestock and forestry production. Our database provides the land-use classes for 513 fields by month for one Brazilian crop year (between October 2015 and July 2016). This information was gathered during two field campaigns in Campo Verde (December 2015 and May 2016) and by visual interpretation of a time series of Landsat-8/Operational Land Imager (OLI) images using an experienced interpreter. A set of 14 preprocessed synthetic aperture radar Sentinel-1 and 15 Landsat-8/OLI mosaic images is also made available. It is important to promote the use of radar data for tropical agricultural applications, especially because the use of optical remote sensing in these regions is hindered by the high frequency of cloud cover. To demonstrate the utility of our database, results of an experiment conducted using the Sentinel-1 data set are presented.
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    High-Resolution Seismic Time–Frequency Analysis Using the Synchrosqueezing Generalized S-Transform (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: In this letter, a new method is introduced for a seismic time–frequency (TF) analysis. The proposed method is called synchrosqueezing generalized S-transform (SSGST), which belongs to a postprocessing procedure of the GST. The frequency-dependent Gaussian window used in the standard S-transform may be not suitable for real applications. In order to overcome this limitation, the frequency-dependent Gaussian window is replaced by a parameterized function containing three parameters. These three parameters result in flexibility in the variation of TF resolution. Then, the synchrosqueezing transform is employed to squeeze the TF coefficients of the GST to achieve an energy-concentrated TF representation. Synthetic examples and field data show that the SSGST achieves a high resolution and has the potential in highlighting geological structures with high precision.
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    Particle Swarm Optimization for Object-Based Feature Selection of VHSR Satellite Images (2018)
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    Publication Date: 2018-02-28
    Description: With the advent of very high spatial resolution (VHSR) satellite imagery, object-based classification (OBC) has attracted a great deal of attention in the remote sensing community. However, due to the numerous spectral, textural, and structural features which can be extracted for each object, feature selection is needed to address the curse-of-dimensionality issue. In this letter, a new wrapper object-based feature (OBF) selection method was applied in order to select the best combination of spectral, textural, and structural OBFs. In the first step, the image is segmented into objects using the multiresolution segmentation method. Subsequently, OBFs are extracted for each object. Last, optimal OBFs are selected using the proposed method based on particle swarm optimization (PSO) combined with the minimum distance classifier. Two VHSR remotely sensed images, acquired by a WorldView-2 sensor, were used to test the proposed method. The results of the experiment indicated that OBC using optimal OBFs led to significant improvement over the case where all extracted features were used. Furthermore, PSO has a better performance than do the three metaheuristic optimization algorithms: genetic algorithm, artificial bee colony, and honey-bee mating, according to the technique of order of preference by similarity to ideal solution analysis.
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    On the Use of the Expanded Image in Quality Assessment of Pansharpened Images (2018)
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    Publication Date: 2018-02-28
    Description: This letter discusses whether the expanded multispectral image, i.e., the original multispectral image upsampled to the panchromatic scale, can be used during the assessment of the quality of pansharpened multispectral images. By considering Wald’s protocol, the authors demonstrate that the adoption of the expanded image as the reference is erroneous and brings a quality assessment of the fused images that is misleading. In addition, some recommendations about the valid role of the expanded image are provided. The discussion is supported by a quantitative analysis and visual comparisons.
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    A Novel Automatic PolSAR Ship Detection Method Based on Superpixel-Level Local Information Measurement (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: To detect ships robustly and automatically in monitoring the marine areas, polarimetric synthetic aperture radar imagery is more and more important. In this letter, three superpixel-level dissimilarity measures are developed to enhance the contrast between ship targets and sea clutter, which are then used to construct an automatic detection algorithm. In the proposed method, multiscale superpixels are first generated. Second, the measurements between a certain superpixel and surrounding ones are calculated. The dissimilarity measures are then transformed from the superpixel level to the pixel level. Third, kernel Fisher discriminant analysis is utilized to improve the separability between ship targets and clutter. Finally, linear support vector machine classifier is utilized to complete the detection automatically. Experiments on the synthetic and real data demonstrate that the proposed method is effective for ship detection with only few false alarms existing, especially under the low signal-to-clutter ratio.
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    Change Detection in Optical Satellite Images Based on Local Binary Similarity Pattern Technique (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: In this letter, a binary descriptor-based approach is proposed to provide change detection in bitemporal optical satellite images. Multitemporal satellite images suffer from illumination variation. In order to tackle this problem, a variant of a local binary similarity pattern (LBSP) descriptor, which has a good resistance to illumination variation, is proposed. The proposed technique consists of two steps: binary feature vector creation and generation of binary change map using Hamming distance as a similarity metric. To get the binary feature vectors, inter-LBSP, which utilizes the region information across images, is applied on both images. Before applying LBSP, both images are partitioned into overlapping patches. This letter proposes two methods taking different combinations of patches at each pixel position. First one uses the center of one image and neighborhood information of two images, whereas the second one uses the center of two images and neighborhood information of one image. Unlike the conventional LBSP technique, a new threshold calculation technique is used for the generation of binary feature vectors. Experiments are conducted on three data sets acquired by the Landsat satellite, and results show that the proposed technique performs better compared with the earlier reported techniques.
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    Shape Parameter Estimator of the Generalized Gaussian Distribution Based on the MoLC (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: A novel estimator for the shape parameter of the generalized Gaussian distribution (GGD) is proposed based on the method of logarithmic cumulants. First, the expression of the log-cumulant for the GGD is theoretically derived. As a result, a simple equation for the estimation of the shape parameter is obtained. The processing procedure of the new estimator for practical applications is also provided. Numerical experiments are used to verify the excellent estimation performances of the proposed estimator for both large and small samples. Moreover, experiments using a measured CARABAS-II data set also validate the superiority of the proposed estimator.
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    Multilabel Conditional Random Field Classification for UAV Images (2018)
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    Publication Date: 2018-02-28
    Description: In this letter, we formulate the multilabeling classification problem of unmanned aerial vehicle (UAV) imagery within a conditional random field (CRF) framework with the aim of exploiting simultaneously spatial contextual information and cross-correlation between labels. The pipeline of the framework consists of two main phases. First, the considered input UAV image is subdivided into a grid of tiles, which are processed thanks to an opportune representation and a multilayer perceptron classifier providing thus tile-wise multilabel prediction probabilities. In the second phase, a multilabel CRF model is applied to integrate spatial correlation between adjacent tiles and the correlation between labels within the same tile, with the objective to improve iteratively the multilabel classification map associated with the considered input UAV image. Experimental results achieved on two different UAV image data sets are reported and discussed.
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    Adaptive Trigonometric Transformation Function With Image Contrast and Color Enhancement: Application to Unmanned Aerial System Imagery (2018)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2018-02-28
    Description: An unmanned aerial system (UAS)-based imaging technology has gained great interests in modern photogrammetry and remote sensing. However, due to the limitations of UAS imaging devices, image enhancement (IE) has become a necessary process for improving the visual appearance of UAS images. Although a great amount of effort has been focused on improving image quality from different aspects, the major obstacles are from computational efficiency and complexity, such as manually adjusting the associated algorithmic parameters that account for various image luminance. To overcome these drawbacks, we propose a new adaptive yet highly efficient luminance enhancement method, namely, adaptive trigonometric transformation function (ATTF), for enhancing the visual quality of digital color images captured by a UAS. The ATTF is derived from a tangent-based transformation function whose characteristics adaptively change with respect to the variation of the image luminance. By combining ATTF with a Laplacian operator and a color restoration process, a well-balanced color image is obtained. The effectiveness of the proposed technique is evaluated on various UAS-based images and compared with other IE techniques.
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    Estimation and Mitigation of Time-Variant RFI in Low-Frequency Ultra-Wideband Radar (2018)
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    Publication Date: 2018-02-28
    Description: The work presented in this letter focuses on the time-variant radio frequency interference (RFI) issue in the low-frequency ultra-wideband (UWB) radar. Different from many previous studies, we first analyze the characteristics of RFIs and scattered echoes in the slow-time dimension and take advantage of overlapped short-time Fourier transform to adapt to the time-variant RFIs and update the frequency Doppler spectrum. Then, in the frequency Doppler spectrum, we adopt the minimum statistic combined with 1-D cell-averaging constant false alarm rate to estimate and separate the RFI power spectrum from the scattered echoes based on their differences. Finally, to mitigate the estimated RFIs, a suboptimal filter controlled by the defined entropy of range profiles after math filtering is designed. Employing a UWB radar, different experiments were conducted, and results verify the proposed method.
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    A Better View Over Titan Drainage Networks Through RGB Fusion of Cassini SAR Images (2018)
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    Publication Date: 2018-02-28
    Description: We present a simple method to enhance the view of Titan drainage networks, thus allowing extracting relevant hydrological parameters. The method uses RGB fusion of three Cassini synthetic aperture radar images acquired at different times, and is tested on one drainage network. Comparison with previous studies of the same network shows an increase in all the measured parameters. The present results help constrain previous estimates of erosion times, terrain, and tectonic models for the area and indicate that, whenever possible, geomorphological inference from drainage network geometry should be drawn on multiply sampled scenes.
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    Azimuth Multichannel GMTI Based on Ka-Band DBF-SCORE SAR System (2018)
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    Publication Date: 2018-02-28
    Description: The azimuth multichannel synthetic aperture radar (SAR) system is used in ground moving target indication (GMTI). But for Ka-band SAR, the conflicts between high gain and wide swath coverage are very strict. The digital beamforming (DBF) scan-on-receive (SCORE) algorithm can solve this problem very well. This letter introduces a multichannel SAR system with three receiving antennas in the azimuth direction. For each azimuth antenna, eight subapertures are arranged in the elevation direction in order to obtain higher gain and a lower data rate through the DBF-SCORE algorithm. Airborne flight experiments with this system are accomplished successfully and quantities of experimental data are acquired. In this letter, we first derive the signal forms and weighting coefficients in the DBF-SCORE procedures. Then, different GMTI methods including along-track interferometry and displaced phase center antenna are introduced for azimuth multichannel SAR–GMTI based on the DBF-SCORE technology. Finally, airborne raw data with cooperating moving targets are processed successfully, and the estimation errors of the radial velocities meet the expected accuracy requirements.
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    A Tool for Analysis and Calibration of Compact Polarimetry SAR Mode Anomaly (2018)
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    Publication Date: 2018-02-28
    Description: Transmit wave polarization (i.e., ellipticity and roll) deviation from the ideal or intended transmit mode affects compact polarimetry (CP) products and associated data exploitation. Hence, it is important to evaluate such effects on the CP model and account for the associated variations through data exploitations. This letter explores an approach to model and estimate ellipticity variations for calibration by taking advantage of a variational model and adjoint polarimetric synthetic aperture radar data.
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    Knowledge-Aided Ocean Clutter Suppression Method for Sky-Wave Over-the-Horizon Radar (2018)
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    Publication Date: 2018-02-28
    Description: In a sky-wave radar, the strong ocean clutter may cover up the echo signal of slow-speed targets. This letter proposed a knowledge-aided ocean clutter suppression method for the sky-wave radar. The proposed method uses the radar carrier frequency and the pulse repetition interval as prior knowledge to reconstruct the prior ocean clutter. This reconstructed clutter is combined with the ionosphere phase perturbation model. The resulting prior clutter is included in the optimal filter design. The simulation results show that the output signal-to-clutter plus noise ratio of this proposed method is 2.507 dB larger than the methods proposed by others.
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    Novel Form of MMSE Estimation via Exact Moment Value of Noise and Higher Order Taylor Series in AWGN (2018)
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    Publication Date: 2018-02-28
    Description: Minimum mean-square-error (MMSE) and maximum a posteriori (MAP) estimations are considered basic approaches commonly used in denoising methods. In most cases, MMSE estimation yields better performance in reducing noise than MAP estimation does. However, the calculation according to MMSE estimation is more difficult than that of MAP estimation, because MMSE estimation relies on integration and its results mainly contain special functions, while MAP estimation relies on differentiation. The previous research work presented MMSE estimation in the form of differentiation, which is in the approximation form. But in this letter, we will present MMSE estimation in the differentiation form by using the actual moment value of the probability density function of Gaussian noise together with the higher order Taylor series. This will provide higher efficiency of noise reduction than the formerly presented method.
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    Ship Detection From Thermal Remote Sensing Imagery Through Region-Based Deep Forest (2018)
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    Publication Date: 2018-02-28
    Description: Ship detection from thermal remote sensing imagery is a challenging task because of cluttered scenes and variable appearances of ships. In this letter, we propose a novel detection algorithm named region-based deep forest (RDF) toward overcoming these existing issues. The RDF consists of a simple region proposal network and a deep forest ensemble. The region proposal network trained over gradient features robustly generates a small number of candidates that precisely cover ship targets in various backgrounds. The deep forest ensemble adaptively learns features from remote sensing data and discriminates real ships from region proposals efficiently. The training process of deep forest ensemble is efficient and users can control training cost according to computational resource available. Experimental results on numerous thermal satellite images demonstrate the superior performance of our method compared with state-of-the-art methods.
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    Airport Detection in Large-Scale SAR Images via Line Segment Grouping and Saliency Analysis (2018)
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    Publication Date: 2018-02-28
    Description: The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods locate airports by connecting pairs of line segments or directly applying saliency analysis to an entire SAR image. These methods are either time-consuming or can easily result in false detection. Considering these issues, a method using line segment grouping and saliency analysis is proposed in this letter. First, line segments are obtained via an improved line segment detector (LSD). After line segment grouping, airport support regions are extracted. Then, selective nonmaximum suppression is proposed to obtain potential airport regions. Finally, airport regions are located by false alarm control and saliency analysis. Experiments on large-scale SAR images prove that our proposed algorithm has a better performance and higher efficiency in airport detection compared with traditional methods.
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    Frequency Calibration for the Scattering Matrix of the Simultaneous Polarimetric Radar (2018)
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    Publication Date: 2018-02-28
    Description: In order to obtain precise measurements of the target scattering matrix, the simultaneous fully polarimetric radar imposes strict requirements on the frequency stability in terms of accurate sampling rate and intermediate frequency (IF). In this letter, the effect of frequency stability on the fully polarimetric radar with a pair of orthogonal linear frequency modulated waveforms is analyzed. Furthermore, a calibration method is proposed to calibrate the IF and the sampling rate. The calibration results demonstrate and verify the validity of the proposed method.
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    Active Learning Improved by Neighborhoods and Superpixels for Hyperspectral Image Classification (2018)
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    Publication Date: 2018-02-28
    Description: Active learning (AL) is a promising solution to hyperspectral image classification with very few initial labeled samples. Although previous AL heuristics have exhibited encouraging results, some challenges are still open. On the one hand, traditional AL heuristics measured uncertainty only in feature domain (i.e., spectral or spectral–spatial features) with a pixelwise manner, which ignores the spatial uncertainty. On the other hand, traditional batch-mode AL methods rarely considered spatial homogeneity, since they selected a batch of samples from the candidates, which will induce redundancy unavoidably. To overcome these issues, we first propose an enhanced uncertainty measure considering the neighborhood information. We then propose to use simple linear iterative clustering for generating superpixels, where the selected batch samples are constrained to be from different superpixels, which improves the diversity of the selected samples. The experimental results with two popular hyperspectral data sets indicate that the proposed methods can significantly improve the classification accuracy compared with the traditional methods.
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    Ship Classification in SAR Images Improved by AIS Knowledge Transfer (2018)
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    Publication Date: 2018-02-28
    Description: A major bottleneck in limiting the application of the existing methods of ship classification in synthetic aperture radar (SAR) images is the inadequate amount of labeled data available for training a classifier. However, generating ground truth involves expensive and time-consuming ground campaigns or is costly, since a high number of SAR image acquisition will be necessary. In contrast, an automatic identification system (AIS), which is an automatic tracking system used for monitoring maritime ships, can provide plenty of labeled ship samples that is relatively easier to be obtained. Inspired by these facts, this letter proposes to improve ship classification in SAR images by transferring AIS knowledge. We propose an improved multiclass adaptive support vector machine, combined with the naive geometric features (NGFs), to achieve transfer learning between the AIS domain and the SAR image domain. The experiments prove that the traditional method can be significantly improved by AIS information transfer, especially when only a few training samples in the SAR domain are available. In addition, it also shows that after feature selection, the performance of the proposed method can be close to that of the state of the art, even if by only using simpler NGFs and few training samples.
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    Evaluating IMERG V04 Final Run for Monitoring Three Heavy Rain Events Over Mainland China in 2016 (2018)
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    Publication Date: 2018-02-28
    Description: Predicting and monitoring the spatiotemporal characteristics of heavy rain events are important to hazard preparedness, mitigation efforts, and local water resource management. Using three data sets, namely, the daily rain product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 04 Final Run, the daily output from European Centre for Medium-Range Weather Forecasts reanalysis data Interim version (ERA-Interim), and the high-quality gauge-satellite merged precipitation product, the spatiotemporal patterns of three heavy rain events are investigated for the first time over China in 2016, with the objective of assessing the capability of IMERG product for monitoring heavy rain events. It is found that the daily IMERG Final Run can better capture the spatial and temporal characteristics of heavy rain compared with that from ERA-Interim, but it significantly overestimates the amounts of the heaviest rainfalls by 11%–85% over the example regions. The comparison of regional averaged precipitation demonstrates that time series of precipitation retrieved by the IMERG algorithm agree well with that from gauge-satellite merged data set, with differences less than 10 mm on most days over each region. The statistic metrics demonstrate that the IMERG Final Run has a strong potential for detecting heavy rain events but with a relatively large error. This letter may provide useful feedback and insights for further improving the precipitation retrieving algorithm and the application of such data sets.
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    Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1 (2018)
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    Publication Date: 2018-02-28
    Description: Mapping winter vegetation quality is a challenging problem in remote sensing. This is due to cloud coverage in winter periods, leading to a more intensive use of radar rather than optical images. The aim of this letter is to provide a better understanding of the capabilities of Sentinel-1 radar images for winter vegetation quality mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Charentes-Maritimes, France. This data set was processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed that the results of the proposed RNN models clearly outperformed classical machine learning approaches (support vector machine and random forest).
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    Introducing IEEE Collabratec (2018)
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    IEEE Geoscience and Remote Sensing Letters information for authors (2018)
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    Focusing of SAR With Curved Trajectory Based on Improved Hyperbolic Range Equation (2018)
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    Publication Date: 2018-02-28
    Description: For a synthetic aperture radar (SAR) system with curved trajectory, which is different from the conventional SAR, the downward velocity and the acceleration result in highly complicated range history, making it hard to achieve a focused target response. The traditional SAR imaging algorithms are not accurate enough to compensate the phase errors introduced from the highly complicated range history. In this letter, considering the impact of complex range history, an improved hyperbolic range equation is proposed to access the 2-D spectrum for curved trajectory SAR imaging. Based on the derived spectrum, frequency-domain imaging algorithm can be performed to focus targets. By analyzing the phase error and comparing with other current range models, the proposed range model is proved to be precise enough to deal with the complex motion model happened in curved trajectory SAR imaging. Simulation experiments are implemented to evaluate the imaging performance of the proposed approach.
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    IEEE Open Access (2018)
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    IEEE Geoscience and Remote Sensing Letters Institutional Listings (2018)
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    [Front cover/Table of contents] (2018)
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    Publication Date: 2018-01-27
    Description: Presents the cover/table of contents for this issue of the periodical.
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    Table of contents (2018)
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    Publication Date: 2018-01-27
    Description: Presents the table of contents for this issue of the periodical.
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    Atmospheric Humidity Sounding Using Differential Absorption Radar Near 183 GHz (2018)
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    Publication Date: 2018-01-27
    Description: A tunable G-band frequency-modulated continuous-wave radar system has been developed and used to perform differential absorption atmospheric humidity measurements for the first time. The radar’s transmitter uses high- power-handling GaAs Schottky diodes to generate between 15–23 dBm over a 10-GHz bandwidth near 183 GHz. By virtue of a high-isolation circular polarization duplexer, the monostatic radar’s receiver maintains a noise figure of about 7 dB even while the transmitter is on. With an antenna gain of 40 dB, high-SNR detection of light rain is achieved out to several hundred meters distance. Owing to the strong spectral dependence of the atmospheric absorption over the upper flank of the 183-GHz water absorption line, range-resolved measurements of absolute humidity can be obtained by ratioing the rain echoes over both range and frequency. Absorption measurements obtained are consistent with models of atmospheric millimeter-wave attenuation, and they demonstrate a new method for improving the accuracy of humidity measurements inside of clouds.
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    Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks (2018)
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    Publication Date: 2018-01-27
    Description: Semantic segmentation of aerial images refers to assigning one land cover category to each pixel. This is a challenging task due to the great differences in the appearances of ground objects. Many attempts have been made during the past decades. In recent years, convolutional neural networks (CNNs) have been introduced in the remote sensing field, and various solutions have been proposed to realize dense semantic labeling with CNNs. In this letter, we propose shuffling CNNs to realize semantic segmentation of aerial images in a periodic shuffling manner. This approach is a supplement to current methods for semantic segmentation of aerial images. We propose a naive version and a deeper version of this method, and both are adept at detecting small objects. Additionally, we propose a method called field-of-view (FoV) enhancement that can enhance the predictions. This method can be applied to various networks, and our experiments verify its effectiveness. The final results are further improved through an ensemble method that averages the score maps generated by the models at different checkpoints of the same network. We evaluate our models using the ISPRS Vaihingen and Potsdam data sets, and we acquire promising results using these two data sets.
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    Scene Classification Based on Two-Stage Deep Feature Fusion (2018)
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    Publication Date: 2018-01-27
    Description: In convolutional neural networks (CNNs), higher layer information is more abstract and more task specific, so people usually concern themselves with fully connected (FC) layer features, believing that lower layer features are less discriminative. However, a few researchers showed that the lower layers also provide very rich and powerful information for image representation. In view of these study findings, in this letter, we attempt to adaptively and explicitly combine the activations from intermediate and FC layers to generate a new CNN with directed acyclic graph topology, which is called the converted CNN. After that, two converted CNNs are integrated together to further improve the classification performance. We validate our proposed two-stage deep feature fusion model over two publicly available remote sensing data sets, and achieve a state-of-the-art performance in scene classification tasks.
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    Spectral Unmixing With Multiple Dictionaries (2018)
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    Publication Date: 2018-01-27
    Description: Spectral unmixing aims at recovering the spectral signatures of materials, called endmembers, mixed in a hyperspectral image (HSI) or multispectral image, along with their abundances. A typical assumption is that the image contains one pure pixel per endmember, in which case spectral unmixing reduces to identifying these pixels. Many fully automated methods have been proposed in recent years, but little work has been done to allow users to select areas where pure pixels are present manually or using a segmentation algorithm. Additionally, in a nonblind approach, several spectral libraries may be available rather than a single one, with a fixed number (or an upper or lower bound) of endmembers to chose from each. In this letter, we propose a multiple-dictionary constrained low-rank matrix approximation model that addresses these two problems. We propose an algorithm to compute this model, dubbed multiple matching pursuit alternating least squares, and its performance is discussed on both synthetic and real HSIs.
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    Development and Assessment of a Data Set Containing Frame Images and Dense Airborne Laser Scanning Point Clouds (2018)
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    Publication Date: 2018-01-27
    Description: This letter describes the main features of a data set that contains aerial images acquired with a medium format digital camera and point clouds collected using an airborne laser scanning unit, as well as ground control points and direct georeferencing data. The flights were performed in 2014 over an urban area in Presidente Prudente, São Paulo, Brazil, using different flight heights. These flights covered several features of interest for research, including buildings of different sizes and roof materials, roads, and vegetation. Three point clouds with different densities, a block of digital aerial images, and auxiliary data are available. A geometric assessment was conducted to ensure the accuracy and consistency of the data, and an RMSE of 7 cm was achieved using bundle block adjustment. The data set is freely available for download, and it will be expanded with data collected over time.
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    One-Dimensional Mirrored Aperture Synthesis With Rotating Reflector (2018)
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    Publication Date: 2018-01-27
    Description: In this letter, 1-D mirrored aperture synthesis with a rotating reflector (1-D MAS-R) is proposed to improve the spatial resolution and reduce the number of required antennas for passive microwave remote sensing. The principle of the 1-D MAS-R with an antenna array is given, and from the principle, the 1-D MAS-R with only one antenna can also reconstruct the image of the scene. Simulation results demonstrate the validity of the 1-D MAS-R even with only one antenna, and the spatial resolution is improved by increasing the distance between the reflector and the antenna or antenna array.
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    Mapping the Spatiotemporal Dynamics of Europe’s Land Surface Temperatures (2018)
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    Publication Date: 2018-01-27
    Description: The land surface temperature (LST) drives many terrestrial biophysical processes and varies rapidly in space and time primarily due to the earth’s diurnal and annual cycles. Models of the diurnal and annual LST cycle retrieved from satellite data can be reduced to several gap-free parameters that represent the surface’s thermal characteristics and provide a generalized characterization of the LST temporal dynamics. In this letter, we use such an approach to map Europe’s annual and diurnal LST dynamics. In particular, we reduce a five-year time series (2009–2013) of diurnal LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to 48 sets of half-hourly annual cycle parameters (ACPs), namely, the mean annual LST, the yearly amplitude of LST, and the LST phase shift from the spring equinox. The derived data provide a complete representation of how mainland Europe responds to the heating of the sun and the nighttime LST decay and reveal how Europe’s biogeographic regions differ in that respect. We further argue that the SEVIRI ACP can provide an observation-based spatially consistent background for studying and characterizing the thermal behavior of the surface and also a data set to support climate classification at a finer spatial resolution.
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    Object Tracking in Satellite Videos by Fusing the Kernel Correlation Filter and the Three-Frame-Difference Algorithm (2018)
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    Publication Date: 2018-01-27
    Description: Object tracking is a popular topic in the field of computer vision. The detailed spatial information provided by a very high resolution remote sensing sensor makes it possible to track targets of interest in satellite videos. In recent years, correlation filters have yielded promising results. However, in terms of dealing with object tracking in satellite videos, the kernel correlation filter (KCF) tracker achieves poor results due to the fact that the size of each target is too small compared with the entire image, and the target and the background are very similar. Therefore, in this letter, we propose a new object tracking method for satellite videos by fusing the KCF tracker and a three-frame-difference algorithm. A specific strategy is proposed herein for taking advantage of the KCF tracker and the three-frame-difference algorithm to build a strong tracker. We evaluate the proposed method in three satellite videos and show its superiority to other state-of-the-art tracking methods.
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    A CFCC-LSTM Model for Sea Surface Temperature Prediction (2018)
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    Publication Date: 2018-01-27
    Description: Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.
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    IEEE Geoscience and Remote Sensing Letters publication information (2018)
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    Publication Date: 2018-01-27
    Description: Provides a listing of current staff, committee members and society officers.
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    Noise Performance Comparison Between Continuous Wave and Stroboscopic Pulse Ground Penetrating Radar (2018)
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    Publication Date: 2018-01-27
    Description: Although stroboscopic pulse (SP) ground penetrating radar (GPR) is the most popular and widespread equipment for subsoil investigation, continuous-wave (CW) radar has better performance in terms of noise, system dynamic range, and penetration depth, at the expense of greater complexity and cost of the components. The aim of this letter is a direct comparison between SP GPR and CW GPR through an extensive measurement campaign in five different locations representative of the different conditions where a GPR could operate.
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    Improving TMPA 3B43 V7 Data Sets Using Land-Surface Characteristics and Ground Observations on the Qinghai–Tibet Plateau (2018)
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    Publication Date: 2018-01-27
    Description: The accurate knowledge of precipitation information over the Qinghai–Tibet Plateau, where the rain gauge networks are limited, is vital for various applications. While satellite-based precipitation estimates provide high spatial resolution (0.25°), large uncertainties and systematic anomalies still exist over this critical area. To derive more accurate monthly precipitation estimates, a spatial data-mining algorithm was used to remove the obvious anomalies compared with their neighbors from the original Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) 3B43 V7 data at an annual scale, as the TMPA data are more accurate than other satellite-based precipitation estimates. To supplement the international exchange stations, additional ground observations were used to calibrate and improve the TMPA data with anomalies removed at an annual scale. Finally, a disaggregation strategy was adopted to derive monthly precipitation estimates based on the calibrated TMPA data. We concluded that: 1) the obvious anomalies compared with their neighbors could be removed from the original TMPA data sets and 2) the calibrated results were of a higher quality than the original TMPA data in each month from 2000 to 2013. The improved TMPA 3B43 V7 data sets over the Qinghai–Tibet plateau, named NITMPA3B43_QTP, are available at http://agri.zju.edu.cn/NITMPA3B43_QTP/ .
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    Quantification of the Relationship Between Sea Surface Roughness and the Size of the Glistening Zone for GNSS-R (2018)
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    Publication Date: 2018-01-27
    Description: A formulation of the relationship between sea-surface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry, and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data preprocessing, quality control, and GZ area estimation from the DDM. Next, an example for application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in situ roughness estimates using data sets from the National Data Buoy Center, its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy ( $r=0.73$ ).
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    Localization of Multiple Underwater Objects With Gravity Field and Gravity Gradient Tensor (2018)
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    Publication Date: 2018-01-27
    Description: We present a novel algorithm to locate multiple underwater objects in real time using gravity field vector and gravity gradient tensor signals. This algorithm formulates the task of localization of multiple underwater objects into a regularized nonlinear problem, which is solved with the standard Levenberg–Marquardt algorithm. The regularization parameters are estimated by cross validation. The initial coordinates and masses of these underwater objects are automatically determined by solving a single-object localization problem. A synthetic navigation model with two underwater objects was adopted to validate the proposed algorithm. The results show that it has good stability and antinoise ability for multiple underwater objects localizations.
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    Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks (2018)
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    Publication Date: 2018-01-27
    Description: Because the collection of ground-truth labels is difficult, expensive, and time-consuming, classifying hyperspectral images (HSIs) with few training samples is a challenging problem. In this letter, we propose a novel semisupervised algorithm for the classification of hyperspectral data by training a customized generative adversarial network (GAN) for hyperspectral data. The GAN constructs an adversarial game between a discriminator and a generator. The generator generates samples that are not distinguishable by the discriminator, and the discriminator determines whether or not a sample is composed of real data. We design a semisupervised framework for HSI data based on a 1-D GAN (HSGAN). This framework enables the automatic extraction of spectral features for HSI classification. When HSGAN is trained using unlabeled hyperspectral data, the generator can generate hyperspectral samples that are similar to the real data, while the discriminator contains the features, which can be used to classify hyperspectral data with only a small number of labeled samples. The performance of the HSGAN is evaluated on the Airborne Visible Infrared Imaging Spectrometer image data, and the results show that the proposed framework achieves very promising results with a small number of labeled samples.
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    Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR (2018)
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    Publication Date: 2018-01-27
    Description: This letter presents an approach for the detection of moving point targets on high-frame-rate image sequences with low spatial resolution and low SNR based on higher order statistical theory. We propose a novel method for analyzing the time-domain evolution of image data for distinguishing between the background and the target in situations when the spatial signal of the target is swamped by noise. Our method is formulated to detect a time-domain transient signal of unknown scale and arrival time in noisy background. We proposed a bispectrum-based model to characterize the temporal behavior of pixels, and the detection ability under different frame rates and SNRs is analyzed. The method is evaluated using both simulated and real-world data, and we provide a comparison to other widely used point target detection approaches. Our experimental results demonstrate that our algorithm can efficiently detect extremely low SNR targets that are virtually invisible to humans based on time-domain analysis of image sequences.
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    Remote Sensing Image Registration Using Convolutional Neural Network Features (2018)
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    Publication Date: 2018-01-27
    Description: Successful remote sensing image registration is an important step for many remote sensing applications. The scale-invariant feature transform (SIFT) is a well-known method for remote sensing image registration, with many variants of SIFT proposed. However, it only uses local low-level information, and loses much middle- or high-level information to register. Image features extracted by a convolutional neural network (CNN) have achieved the state-of-the-art performance for image classification and retrieval problems, and can provide much middle- and high-level information for remote sensing image registration. Hence, in this letter, we investigate how to calculate the CNN feature, and study the way to fuse SIFT and CNN features for remote sensing image registration. The experimental results demonstrate that the proposed method yields a better registration performance in terms of both the aligning accuracy and the number of correct correspondences.
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    Micro-Doppler Mini-UAV Classification Using Empirical-Mode Decomposition Features (2018)
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    Publication Date: 2018-01-27
    Description: In this letter, we propose an empirical-mode decomposition (EMD)-based method for automatic multicategory mini-unmanned aerial vehicle (UAV) classification. The radar echo signal is first decomposed into a set of oscillating waveforms by EMD. Then, eight statistical and geometrical features are extracted from the oscillating waveforms to capture the phenomenon of blade flashes. After feature normalization and fusion, a nonlinear support vector machine is trained for target class-label prediction. Our empirical results on real measurement of radar signals show encouraging mini-UAV classification accuracy performance.
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    DropBand: A Simple and Effective Method for Promoting the Scene Classification Accuracy of Convolutional Neural Networks for VHR Remote Sensing Imagery (2018)
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    Publication Date: 2018-01-27
    Description: The dropout and data augmentation techniques are widely used to prevent a convolutional neural network (CNN) from overfitting. However, the dropout technique does not work well when applied to the input channels of neural networks, and data augmentation is usually employed along the image plane. In this letter, we present DropBand, which is a simple and effective method of promoting the classification accuracy of CNNs for very-high-resolution remote sensing image scenes. In DropBand, more training samples are generated by dropping certain spectral bands out of original images. Furthermore, all samples with the same set of spectral bands are collected together to train a base CNN. The final prediction for a test sample is represented by the combination of outputs of all base CNNs. The experimental results for three publicly available data sets, i.e., the SAT-4, SAT-6, and UC-Merced image data sets, show that DropBand can significantly improve the classification accuracy of a CNN.
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    Integration of Contextual Knowledge in Unsupervised Subpixel Classification: Semivariogram and Pixel-Affinity Based Approaches (2018)
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    Publication Date: 2018-01-27
    Description: This letter investigates the use of coarse-image features for predicting class labels at a given finer spatial scale. In this regard, two unsupervised subpixel mapping approaches, a semivariogram method, and a pixel-affinity based method are proposed. Furthermore, segmentation-based spectral unmixing is explored so as to address the spectral variability and nonconvexity of classes. In addition, the gradient information is employed to resolve uncertainties in the unmixing process. The proposed modifications based on pixel-affinity and semivariogram have produced an accuracy improvement of 5% or more over the state-of-the-art approaches.
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    Optimal and Suboptimal Velocity Estimators for ArcSAR With Distributed Target (2018)
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    Publication Date: 2018-01-27
    Description: Two new methods of radial velocity estimation for distributed targets in arc-scanning synthetic aperture radar (ArcSAR) systems, namely, the maximum-likelihood estimator (MLE) and the suboptimal method based on the least squares estimation (LSE), are proposed, derived, and analyzed. To this end, we establish that $n$ scatterers of the distributed target are uniformly dispersed within the radar resolution cell of dimensions $a times b$ and they move randomly at different velocities. Furthermore, the effect of the antenna pattern is considered to characterize the amplitude of the scattered signal. Thus, from the coherent integration of the scatters at each pulse repetition interval in radar scanning, $m$ data sequences are obtained as samples of the composite signal, which follows a multivariate normal distribution. From this, the covariance matrix, upon which the methods are based, is derived. Simulations have been carried out to compare the new methods with existing methods, namely, phase, energy, and correlation, as a function of the signal-to-noise ratio. Finally, the results show that the MLE and LSE methods outperform the conventional methods, providing a gain of more than 10 dB.
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    PSOSAC: Particle Swarm Optimization Sample Consensus Algorithm for Remote Sensing Image Registration (2018)
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    Publication Date: 2018-01-27
    Description: Image registration is an important preprocessing step for many remote sensing image processing applications, and its result will affect the performance of the follow-up procedures. Establishing reliable matches is a key issue in point matching-based image registration. Due to the significant intensity mapping difference between remote sensing images, it may be difficult to find enough correct matches from the tentative matches. In this letter, particle swarm optimization (PSO) sample consensus algorithm is proposed for remote sensing image registration. Different from random sample consensus (RANSAC) algorithm, the proposed method directly samples the modal transformation parameter rather than randomly selecting tentative matches. Thus, the proposed method is less sensitive to the correct rate than RANSAC, and it has the ability to handle lower correct rate and more matches. Meanwhile, PSO is utilized to optimize parameter as its efficiency. The proposed method is tested on several multisensor remote sensing image pairs. The experimental results indicate that the proposed method yields a better registration performance in terms of both the number of correct matches and aligning accuracy.
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    Aircraft Type Recognition Based on Segmentation With Deep Convolutional Neural Networks (2018)
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    Publication Date: 2018-01-27
    Description: Aircraft type recognition in remote sensing images is a meaningful task. It remains challenging due to the difficulty of obtaining appropriate representation of aircrafts for recognition. To solve this problem, we propose a novel aircraft type recognition framework based on deep convolutional neural networks. First, an aircraft segmentation network is designed to obtain refined aircraft segmentation results which provide significant details to distinguish different aircrafts. Then, a keypoints’ detection network is proposed to acquire aircrafts’ directions and bounding boxes, which are used to align the segmentation results. A new multirotation refinement method is carefully designed to further improve the keypoints’ precision. At last, we apply a template matching method to identify aircrafts, and the intersection over union is adopted to evaluate the similarity between segmentation results and templates. The proposed framework takes advantage of both shape and scale information of aircrafts for recognition. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 95.6% accuracy on the challenging data set.
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